fit.egp {mev} | R Documentation |
Parameter stability plot and maximum likelihood routine for extended GP models
Description
The function tstab.egp
provides classical threshold stability plot for (\kappa
, \sigma
, \xi
).
The fitted parameter values are displayed with pointwise normal 95% confidence intervals.
The function returns an invisible list with parameter estimates and standard errors, and p-values for the Wald test that \kappa=1
.
The plot is for the modified scale (as in the generalised Pareto model) and as such it is possible that the modified scale be negative.
tstab.egp
can also be used to fit the model to multiple thresholds.
Usage
fit.egp(xdat, thresh, model = c("egp1", "egp2", "egp3"), init, show = FALSE)
tstab.egp(
xdat,
thresh,
model = c("egp1", "egp2", "egp3"),
plots = 1:3,
umin,
umax,
nint,
changepar = TRUE,
...
)
Arguments
xdat |
vector of observations, greater than the threshold |
thresh |
threshold value |
model |
a string indicating which extended family to fit |
init |
vector of initial values, with |
show |
logical; if |
plots |
vector of integers specifying which parameter stability to plot (if any); passing |
umin |
optional minimum value considered for threshold (if |
umax |
optional maximum value considered for threshold (if |
nint |
optional integer number specifying the number of thresholds to test. |
changepar |
logical; if |
... |
additional arguments for the plot function, currently ignored |
Details
fit.egp
is a numerical optimization routine to fit the extended generalised Pareto models of Papastathopoulos and Tawn (2013),
using maximum likelihood estimation.
Value
fit.egp
outputs the list returned by optim, which contains the parameter values, the hessian and in addition the standard errors
tstab.egp
returns a plot(s) of the parameters fit over the range of provided thresholds, with pointwise normal confidence intervals; the function also returns an invisible list containing notably the matrix of point estimates (par
) and standard errors (se
).
Author(s)
Leo Belzile
References
Papastathopoulos, I. and J. Tawn (2013). Extended generalised Pareto models for tail estimation, Journal of Statistical Planning and Inference 143(3), 131–143.
Examples
xdat <- mev::rgp(
n = 100,
loc = 0,
scale = 1,
shape = 0.5)
fitted <- fit.egp(
xdat = xdat,
thresh = 1,
model = "egp2",
show = TRUE)
thresh <- mev::qgp(seq(0.1, 0.5, by = 0.05), 0, 1, 0.5)
tstab.egp(
xdat = xdat,
thresh = thresh,
model = "egp2",
plots = 1:3)